Senior Data Scientist

Monstro•Denver, CO

5h•$159,000 - $225,000•Hybrid

About The Position

Monstro is an AI-native fintech platform reimagining how people and institutions manage money. We’re building a modern foundation for financial decision-making—combining intelligence, automation, and elegant design to help users make smarter choices with confidence. Our team includes experienced builders from leading fintech, wealth management, and technology companies, united by a shared goal: to create a category-defining product that transforms how financial insight is delivered and acted on. About the Role We're looking for an experienced Senior Data Scientist join our founding team and help design and build the intelligence at the heart of our platform. You'll develop the models that transform complex, interconnected financial data into personalized insights and recommendations that help users take control of their financial lives. This role sits at the intersection of cutting-edge ML research and real-world product impact—your work will directly shape how millions of people understand and act on their finances.

Requirements

7+ years of experience in data science, machine learning, or a related quantitative field
Advanced degree (MS or PhD) in Statistics, Computer Science, Economics, or a related quantitative discipline
Strong proficiency in Python and SQL
Deep expertise in machine learning—supervised and unsupervised methods, deep learning, and model evaluation
Experience building recommendation systems or personalization engines
Solid foundation in statistics, experimental design, and causal inference
Experience with time-series analysis and forecasting techniques
Familiarity with NLP techniques and working with unstructured data
Experience with ML frameworks (PyTorch, TensorFlow, scikit-learn)
Hands-on experience with Apache Spark for large-scale data processing
Understanding of production ML—model deployment, monitoring, and iteration
Strong communication skills with the ability to translate technical concepts for diverse audiences
Genuine curiosity and passion for using data to solve meaningful problems

Nice To Haves

Background in fintech, wealth management, or financial services
Experience with reinforcement learning or multi-armed bandits for optimization
Familiarity with Apache ecosystem tools (Kafka, Flink, Airflow)
Experience with feature stores and real-time feature serving
Understanding of financial data, regulations, and privacy requirements
Experience deploying models within enterprise or on-premise environments

Responsibilities

Design and build recommendation systems that deliver tailored financial insights and next-best-actions to users.
Develop models that learn from user behavior, financial patterns, and life events to surface relevant, timely recommendations.
Balance personalization with diversity to help users discover opportunities they wouldn't find on their own.
Build predictive models that anticipate user needs, identify risks, and surface opportunities across their financial landscape.
Apply time-series techniques to forecast trends, detect anomalies, and model financial trajectories.
Develop early warning systems that help users stay ahead of potential issues.
Design and analyze experiments to measure the impact of product changes and model improvements.
Apply causal inference methods—A/B testing, quasi-experimental designs, and observational techniques—to understand what drives outcomes and inform product strategy.
Establish rigorous experimentation practices across the organization.
Extract insights from unstructured financial data—documents, communications, and text sources—using NLP techniques.
Build models for entity extraction, classification, and semantic understanding that enrich our data assets and power intelligent features.
Partner with Data Engineers to define and build features that power ML models.
Work with event-driven data pipelines and feature stores to ensure models have access to real-time, high-quality features.
Iterate rapidly on model architectures, balancing accuracy with interpretability and latency requirements.
Collaborate with ML Engineers and Data Engineers to deploy models to production.
Design for scale, monitoring, and graceful degradation.
Establish feedback loops that enable continuous model improvement based on real-world performance.
Mentor junior data scientists and analysts, raising the bar for scientific rigor across the team.
Collaborate with product managers, designers, and engineers to translate business problems into data science solutions.
Communicate complex findings to technical and non-technical stakeholders.